Description Usage Arguments Details Value Examples
The main algorithm in pirate package for calculating the coefficients of the linear combination of the covariates to generate a GEM. This function can be applied to data sets with more than two treatment groups. See more detail in E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics, (First published online: July 27, 2016). doi: 10.1093/biostatistics/kxw035.
1  gem_fit(dat, method = "F")

dat 
Data frame with first column as the treatment index, second column as the outcome, and the remaining columns as the covariates design matrix. The elements of the treatment index take K distinct values, where K is the number of treatment groups. The outcome has to be a continuous variable. 
method 
Choice of the criterion that the generated effect modifier optimizes. This is a string in

gemObject
is a list of three elements. The first element is the
calculated weight α for combining the predictors X. The second element contains the K vectors of coefficients (γ_{j0},γ_{j1}) from
y_j = γ_{j0} + (Xα)γ_{j1} + ε, j = 1,...,K,
for the K treatment groups respectively. The third element contains the K vectors of coefficients from the unconstraint linear regression models
y_j = β_{j0} + Xβ_{j1} + ε, j = 1,...,K,
for the K treatment groups respectively.
method
The criterion used to generate the GEM
gemObject
Fitted result for the GEM model, see more in Details
p_value
The pvalue for the interaction term in model Y = a + trt + Z + trt*Z + ε, where Z is the GEM
Augmented_Data
The input data argumented with the GEM as the last column
effect.size
The effect size of the GEM if there are only two treatment groups
plot
A scatter plot of Y versus the GEM with fitted lines and grouped by treatment
1 2 3 4 5 6 7 8 9 10  #constructing the covariance matrix
co < matrix(0.2, 10, 10)
diag(co) < 1
dataEx < data_generator1(d = 0.3, R2 = 0.5, v2 = 1, n = 300,
co = co, beta1 = rep(1,10),inter = c(0,0))
#fit the GEM
dat < dataEx[[1]]
model_nu < gem_fit(dat = dat, method = "nu")
model_de < gem_fit(dat = dat, method = "de")
model_F < gem_fit(dat = dat, method = "F")

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.